The purpose of this notebook is to plot several quality control metrics across clusters, so that we can have an idea of which clusters might be problematic.
library(Seurat)
library(tidyverse)
# Paths
path_to_level_3 <- here::here("scRNA-seq/results/R_objects/level_3/")
path_to_level_3_cell_type <- str_c(path_to_level_3, cell_type, sep = "")
path_to_obj <- str_c(
path_to_level_3_cell_type,
"/",
cell_type,
"_clustered_level_3.rds",
sep = ""
)
# Functions
source(here::here("scRNA-seq/bin/utils.R"))
# Colors
color_palette <- c("black", "gray", "red", "yellow", "plum4", "green4",
"blue", "mediumorchid2", "coral2", "blueviolet",
"indianred4", "deepskyblue1", "dimgray", "deeppink1",
"green", "lightgray", "hotpink1", "gold", "brown",
"mediumvioletred", "mediumaquamarine")
# Point sizes
pt_sizes <- c(
NBC_MBC = 0.15,
GCBC = 0.15,
CD4_T = 0.25,
Cytotoxic = 0.4,
PC = 0.4,
myeloid = 0.6,
FDC = 0.6,
PDC = 1,
epithelial = 1
)
pt_size <- pt_sizes[cell_type]
seurat <- readRDS(path_to_obj)
seurat
## An object of class Seurat
## 37378 features across 78974 samples within 1 assay
## Active assay: RNA (37378 features, 0 variable features)
## 3 dimensional reductions calculated: pca, umap, harmony
umap_clusters <- DimPlot(
seurat,
group.by = "seurat_clusters",
pt.size = pt_size,
cols = color_palette
)
umap_clusters
p_assay <- plot_split_umap(seurat, var = "assay")
p_assay
pDNN_vars <- c("pDNN_hashing", "pDNN_scrublet", "pDNN_union")
pDNN_gg <- purrr::map(pDNN_vars, function(x) {
p <- plot_pDNN(seurat_obj = seurat, pDNN_var = x, pt_size = pt_size)
p
})
pDNN_gg
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# Violin plots
pDNN_violins_gg <- purrr::map(pDNN_vars, function(x) {
p <- VlnPlot(
seurat,
features = x,
pt.size = 0,
group.by = "seurat_clusters",
cols = color_palette
) +
xlab("") +
theme(legend.position = "none")
p
})
pDNN_violins_gg
## [[1]]
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## [[3]]
# Scrublet
seurat$scrublet_predicted_doublet[seurat$scrublet_predicted_doublet == "True"] <- "TRUE"
seurat$scrublet_predicted_doublet[seurat$scrublet_predicted_doublet == "False"] <- "FALSE"
scrublet_gg <- DimPlot(seurat, group.by = "scrublet_predicted_doublet")
scrublet_gg
qc_vars <- c(
"nCount_RNA",
"nFeature_RNA",
"pct_mt",
"pct_ribosomal"
)
qc_gg <- purrr::map(qc_vars, function(x) {
p <- FeaturePlot(seurat, features = x, pt.size = pt_size)
p +
scale_color_viridis_c(option = "magma")
})
qc_gg
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# Violin plots
qc_violins_gg <- purrr::map(qc_vars, function(x) {
p <- VlnPlot(
seurat,
features = x,
pt.size = 0,
group.by = "seurat_clusters",
cols = color_palette
) +
xlab("") +
theme(legend.position = "none")
p
})
qc_violins_gg
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s_gg <- FeaturePlot(seurat, features = "S.Score", pt.size = pt_size) +
scale_color_viridis_c(option = "magma")
s_gg
g2m_gg <- FeaturePlot(seurat, features = "G2M.Score", pt.size = pt_size) +
scale_color_viridis_c(option = "magma")
g2m_gg
# Violin Plots
cc_violins_gg <- purrr::map(c("S.Score", "G2M.Score"), function(x) {
p <- VlnPlot(
seurat,
features = x,
pt.size = 0,
group.by = "seurat_clusters",
cols = color_palette
) +
xlab("") +
theme(legend.position = "none")
p
})
cc_violins_gg
## [[1]]
##
## [[2]]
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.7 (Santiago)
##
## Matrix products: default
## BLAS: /apps/R/3.6.0/lib64/R/lib/libRblas.so
## LAPACK: /home/devel/rmassoni/anaconda3/lib/libmkl_rt.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 Seurat_3.2.0 BiocStyle_2.14.4
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-25 ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2 fs_1.4.1 rstudioapi_0.11 spatstat.data_1.4-3 farver_2.0.3 leiden_0.3.3 listenv_0.8.0 ggrepel_0.8.2 fansi_0.4.1 lubridate_1.7.8 xml2_1.3.2 codetools_0.2-16 splines_3.6.0 knitr_1.28 polyclip_1.10-0 jsonlite_1.7.2 broom_0.5.6 ica_1.0-2 cluster_2.1.0 dbplyr_1.4.4 png_0.1-7 uwot_0.1.8 shiny_1.4.0.2 sctransform_0.2.1 BiocManager_1.30.10 compiler_3.6.0 httr_1.4.2 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18 fastmap_1.0.1 lazyeval_0.2.2 cli_2.0.2 later_1.0.0 htmltools_0.5.1.1 tools_3.6.0 rsvd_1.0.3 igraph_1.2.5 gtable_0.3.0 glue_1.4.1 RANN_2.6.1 reshape2_1.4.4 rappdirs_0.3.1 Rcpp_1.0.6 spatstat_1.64-1 cellranger_1.1.0 vctrs_0.3.6 ape_5.3 nlme_3.1-148
## [55] lmtest_0.9-37 xfun_0.14 globals_0.12.5 rvest_0.3.5 mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3 goftest_1.2-2 future_1.17.0 MASS_7.3-51.6 zoo_1.8-8 scales_1.1.1 hms_0.5.3 promises_1.1.0 spatstat.utils_1.17-0 parallel_3.6.0 RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.16 pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15 stringi_1.4.6 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41 ROCR_1.0-11 tensor_1.5 labeling_0.3 patchwork_1.0.0 htmlwidgets_1.5.1 cowplot_1.0.0 tidyselect_1.1.0 here_0.1 RcppAnnoy_0.0.16 plyr_1.8.6 magrittr_1.5 bookdown_0.19 R6_2.4.1 generics_0.0.2 DBI_1.1.0 withr_2.4.1 pillar_1.4.4 haven_2.3.1 mgcv_1.8-31 fitdistrplus_1.1-1 survival_3.1-12 abind_1.4-5 future.apply_1.5.0 modelr_0.1.8 crayon_1.3.4 KernSmooth_2.23-17
## [109] plotly_4.9.2.1 rmarkdown_2.2 readxl_1.3.1 grid_3.6.0 data.table_1.12.8 blob_1.2.1 reprex_0.3.0 digest_0.6.20 xtable_1.8-4 httpuv_1.5.3.1 munsell_0.5.0 viridisLite_0.3.0